4 papers
Finding Simple Proofs for First-Order Optimization
Daniel Berg Thomsen, Manu Upadhyaya, Baptiste Goujaud +2
Progress in mathematics often requires more than a certificate of truth: it requires proof structures that are transparent, checkable, and reusable. Automated systems can increasin…
A Tight Theory of Error Feedback Algorithms in Distributed Optimization
Daniel Berg Thomsen, Adrien Taylor, Aymeric Dieuleveut
Communication costs are a major bottleneck in distributed learning and first-order optimization. A common approach to alleviate this issue is to compress the gradient information e…
An optimal first-order method for smooth and strongly convex composite optimization and its stationary limit
Manu Upadhyaya, Daniel Berg Thomsen, Aymeric Dieuleveut +1
We introduce Prox-ITEM, an optimal proximal gradient method for minimizing , where is smooth and strongly convex, and is convex, proper, and lower semicontinuous. In t…
Tight analyses of first-order methods with error feedback
Daniel Berg Thomsen, Adrien Taylor, Aymeric Dieuleveut
Communication between agents often constitutes a major computational bottleneck in distributed learning. One of the most common mitigation strategies is to compress the information…